Issue #67

March 5 2015

Editor Picks

Deep Learning at Flickr, Pierre GarriguesPierre Garrigues is a Researcher in Machine Perception and Learning at Flickr and also spoke at the Deep Learning Summit at the end of January to give an insight into how Flickr are automating the labelling of their image libraries using Deep Learning techniques as well as the 10 million uploads which they receive each day...

The Architecture of a Data VisualizationInformation Design is playing an increasingly critical role in everyday journalism. The movement from word and picture to “words within diagrams” is building a new form of truth-telling and storytelling — and with it, a new journalistic aesthetic...

Predicting and Plotting Crime in SeattleI have recently been watching “The Wire” and along with my Amazon Prime membership looking better and better, it’s actually given me some things to think about. Besides making me an expert police detective, it has steadily been making an impact on how I view a city. ...

The JVM Option For Deep Learning When You Are Not Google Or NetflixRealising the potential of large datasets through Deep Learning, for some, may be considered the remit of an exclusive club of organisations with deep, deep pockets and the infrastructure to assemble dedicated teams to venture into areas of research which have not yet been explored by humans. Not so, according to Adam Gibson, Founder of SkyMind...

Mapping Your Music CollectionIn this article we'll explore a neat way of visualizing your MP3 music collection. The end result will be a hexagonal map of all your songs, with similar sounding tracks located next to each other...

The Time Everyone “Corrected” the World’s Smartest Woman
When vos Savant politely responded to a reader’s inquiry on the Monty Hall Problem, a then-relatively-unknown probability puzzle, she never could’ve imagined what would unfold: though her answer was correct, she received over 10,000 letters, many from noted scholars and Ph.Ds, informing her that she was a hare-brained idiot...

Jobs

We’re designing our jobs around the great people we attract, which means you won’t find a listing of your day-to-day duties here. Instead, here’s a little bit about the problem we’re working on...

We have tons of data about what happens during a sale over the course of weeks or months. In some cases, we know second-by-second what’s happening during and after a meeting. We’re building our Data & Analytics team to start mining that data and answer a simple question: why do some deals close and others flop? Imagine what happens if we actually figure it out. That means we know, scientifically, how to have great business meetings and how to close deals. That’s a total a game-changer!

While we’ve been focused on the Design & UX aspects until now, we know that we’re sitting on a goldmine. We’re hiring Data Engineers to build out our data warehouse and Data Scientists to own data mining projects...

Training & Resources

RAD - Outlier Detection on Big Data - Now Open SourceOutlier detection can be a pain point for all data driven companies, especially as data volumes grow. At Netflix we have multiple datasets growing by 10B+ record/day and so there’s a need for automated anomaly detection tools ensuring data quality and identifying suspicious anomalies. Today we are open-sourcing our outlier detection function, called Robust Anomaly Detection (RAD), as part of our Surus project...

ComputerWorld's R for Beginners Hands-On GuideComputerworld's Sharon Machlis has done a great service for the R community — and R especially novices — by creating the on-line Beginner's Guide to R. You can read our overview of her guide from 2013 here, but it's been regularly updated since then and is now available in pdf...